import numpy as np
def digit():
    ienum = [
        [0.0, 9645.982, 11230.408, 12033.347, 12579.3955, 12838.227, 13039.906, 13272.683, 13422.359, 13585.451, 13652.278,
         13787.122, 13855.6455, 13938.518, 13990.775, 14076.086, 14114.756, 14178.674, 14239.445, 14299.797, 14384.391,
         14358.683, 14415.685, 14528.544, 14532.744, 14445.817, 14509.628, 14606.803, 14675.209, 14668.549, 14619.219,
         14731.685, 14726.557, 14744.128, 14797.031, 14863.713, 14822.553, 14832.217, 14862.443, 14888.266, 14862.264,
         14902.206, 14894.275, 14863.638, 15010.794, 14990.466, 15041.195, 14979.636, 15076.349, 15033.503, 15109.016,
         15079.675, 15100.475, 15098.549, 15111.9375, 15120.231, 15127.378, 15168.088, 15259.753, 15259.17, 15245.586],


        [0.0, 10877.746, 12255.301, 13132.883, 13388.323, 13771.947, 13932.639, 14173.43, 14295.862, 14541.654, 14575.033,
         14664.581, 14830.18, 14956.393, 15044.823, 15080.448, 15177.803, 15217.703, 15316.028, 15303.189, 15386.986,
         15387.393, 15452.034, 15485.795, 15560.341, 15630.369, 15619.487, 15588.372, 15707.414, 15726.826, 15775.591,
         15854.636, 15841.756, 15898.221, 15872.771, 15958.433, 15975.815, 15955.276, 16025.127, 16000.911, 16070.127,
         16053.041, 16102.643, 16094.466, 16136.295, 16159.686, 16185.12, 16172.378, 16225.278, 16224.625, 16270.646,
         16249.928, 16321.912, 16309.859, 16334.047, 16311.575, 16362.69, 16361.425, 16356.333, 16427.678, 16406.688],

        # [0.0, 8041.6196, 9288.431, 9599.272, 9957.114, 10089.718, 10272.753, 10400.93, 10462.962, 10622.649, 10725.12,
        #  10770.52, 10770.186, 10913.573, 10985.824, 10983.565, 10998.72, 11170.641, 10954.956, 11058.196, 11142.594,
        #  11078.737, 11118.918, 11043.914, 11115.499, 11096.632, 11243.934, 11257.362, 11217.004, 11257.7295, 11229.2295,
        #  11306.152, 11380.243, 11279.22, 11457.53, 11417.668, 11517.484, 11464.715, 11316.058, 11517.602, 11437.108,
        #  11390.181, 11345.417, 11375.965, 11378.66, 11491.918, 11459.4795, 11491.016, 11609.953, 11588.208, 11614.794,
        #  11586.526, 11610.837, 11556.488, 11467.62, 11587.262, 11579.906, 11552.878, 11505.948, 11554.922, 11587.613],

        [0.0, 9412.201, 10864.04, 11735.083, 12286.138, 12593.905, 12738.768, 13212.185, 13240.471, 13381.612, 13448.869,
         13628.073, 13744.2705, 13771.873, 13903.51, 13920.302, 13944.542, 13958.662, 14064.044, 14110.94, 14129.388,
         14271.805, 14277.472, 14288.877, 14272.662, 14338.727, 14397.81, 14438.459, 14424.216, 14499.365, 14495.547,
         14530.753, 14562.31, 14567.865, 14676.541, 14609.536, 14696.058, 14680.466, 14708.305, 14705.086, 14658.925,
         14698.869, 14750.263, 14766.323, 14750.053, 14787.404, 14806.394, 14794.867, 14804.881, 14872.612, 14877.834,
         14905.031, 14905.266, 14927.76, 14940.489, 14938.75, 14984.1045, 14955.878, 14937.834, 15013.745, 15006.466],

        [0.0, 9431.841, 11166.375, 11848.904, 12309.518, 12729.567, 12962.367, 13141.714, 13249.016, 13363.373, 13518.114,
         13635.842, 13708.771, 13872.558, 13924.425, 13957.919, 13995.617, 14068.656, 14066.112, 14088.273, 14178.677,
         14273.029, 14301.729, 14345.803, 14345.875, 14394.289, 14387.987, 14445.447, 14437.909, 14487.685, 14521.255,
         14512.542, 14572.451, 14573.828, 14511.953, 14671.325, 14602.708, 14727.781, 14734.178, 14703.979, 14741.8125,
         14730.864, 14742.667, 14811.561, 14747.572, 14815.361, 14823.417, 14822.208, 14830.639, 14877.731, 14898.115,
         14855.831, 14887.977, 14925.3, 14925.489, 14920.486, 14935.583, 14971.158, 14976.669, 14989.764, 14989.305],

        [0.0, 9928.039, 11546.732, 12262.502, 12819.138, 13087.21, 13368.398, 13560.193, 13765.724, 13913.294, 13942.356,
         14112.943, 14222.898, 14188.964, 14363.172, 14393.8955, 14420.216, 14534.791, 14588.359, 14575.453, 14716.477,
         14708.592, 14716.916, 14685.628, 14816.683, 14905.947, 14897.0625, 14971.891, 14907.984, 14879.862, 14973.419,
         15032.175, 15035.275, 15048.739, 15072.511, 15105.935, 15139.236, 15195.878, 15184.45, 15137.096, 15216.919,
         15250.75, 15214.057, 15279.669, 15238.01, 15289.719, 15306.826, 15316.885, 15384.997, 15326.189, 15361.033,
         15367.784, 15425.776, 15413.656, 15477.172, 15415.729, 15400.109, 15458.541, 15487.989, 15465.928, 15521.221],

        [0.0, 9376.64, 10594.362, 11764.239, 12220.081, 12398.978, 12602.718, 12771.687, 12930.008, 13247.82, 13284.542,
         13516.864, 13595.319, 13560.063, 13735.176, 13564.853, 13806.211, 13782.824, 13978.109, 13965.378, 13969.715,
         14082.426, 13929.216, 14165.238, 14224.219, 14111.586, 14279.238, 14327.173, 14228.547, 14361.802, 14310.727,
         14481.36, 14328.344, 14264.258, 14360.094, 14431.784, 14519.691, 14388.964, 14517.731, 14499.93, 14515.703,
         14662.507, 14471.933, 14693.648, 14610.536, 14601.222, 14566.05, 14573.864, 14669.46, 14612.081, 14664.8955,
         14672.364, 14745.265, 14675.241, 14807.157, 14854.313, 14770.04, 14784.667, 14739.083, 14707.836, 14799.042]
    ]
    #'LSR KD 2-layer CNN'
    ielabel = ['10-layer cnn KD 2-layer CNN', '2-layer CNN baseline', '4-layer CNN KD 2-layer CNN',
               '6-layer CNN KD 2-layer CNN', '8-layer CNN KD 2-layer CNN', 'fake noise tescher KD 2-layer CNN']
    #'LSR KD 2-layer CNN': 2
    labeldict = {
        '10-layer cnn KD 2-layer CNN': 0,
        '4-layer CNN KD 2-layer CNN': 2,
        '6-layer CNN KD 2-layer CNN': 3,
        '8-layer CNN KD 2-layer CNN': 4,
        '2-layer CNN baseline': 1,
        'fake noise tescher KD 2-layer CNN': 5,

    }
    label_name = {
        '10-layer cnn KD 2-layer CNN': '10-layer KD',
        '4-layer CNN KD 2-layer CNN': '4-layer KD',
        '6-layer CNN KD 2-layer CNN': '6-layer KD',
        '8-layer CNN KD 2-layer CNN': '8-layer KD',
        '2-layer CNN baseline': 'baseline',
        'fake noise tescher KD 2-layer CNN': 'FNT KD',
    }

    res = []
    lb_res = []
    for (lb, num) in enumerate(labeldict):
        if (not 'fake' in num) and (not '6' in num):
            continue
        res.append(list(np.array(ienum[labeldict[num]]) / 10000))
        lb_res.append(label_name[num])

    return res, lb_res